Is Perfect the Enemy of Good? Examining Trends in Perfectionism Research and Mapping the Road Ahead
Bibliographic record
Abstract
Despite the recent upsurge in scholarly interest and the prevalence of perfectionism in contemporary organizations, knowledge of the factors that trigger, reinforce, and ameliorate the (mal)adaptiveness of perfectionism remains underexplored. To overcome this limitation, this symposium presents five interdisciplinary studies that address critical questions in perfectionism scholarship: when is it beneficial or harmful to demand perfectionism in the workplace? What sociopsychological conditions exacerbate or buffer the negative effects of perfectionism on employee behavior? How does leader perfectionism influence interpersonal dynamics at work? The studies combine theoretical, quantitative, and qualitative approaches to examine the nature and influence of perfectionism at the individual, group, and organizational levels of analysis. Doing so advances our understanding of the role of perfectionism in shaping a range of work outcomes, such as in-role and extra-role behaviors, displacement of responsibility, dehumanization, and turnover. By providing a comprehensive analysis of current research trends and identifying unexplored areas of inquiry, this symposium offers important and timely contributions to the understanding of perfectionism in organizations. Implications of perfection and excellence striving in daily work Author: Monique Mohr; Chemnitz University of Technology Author: Carolin Dietz; Chemnitz University of Technology The double-edged sword of perfectionism in in-role and extra-role performance Author: Emily Kleszewski; Philipps-University of Marburg Author: Monique Mohr; Chemnitz University of Technology Dehumanizing and rehumanizing social cues lead to (mal)adaptive perfectionism in professional ballet Author: Rachael Goodwin; Not Associated Author: Lyndon Earl Garrett; University of Melbourne Author: Joel Gardner; Boston College Author: Ali Block; Leader perfectionism and team task interdependence exacerbate displacement of responsibility Author: Lixun Zheng; Tsinghua University Author: Anna Carmella Ocampo; ESADE Business School Author: Lu Wang; University of Alberta Author: Jun Gu; Macquarie University Author: Yanfei Wang; A framework on the organizational consequences of CEO perfectionism Author: Ryan Federo; Universitat Autònoma de Barcelona Author: Paula M Infantes Sanchez; University of Groningen
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".